Low-Rank Subspace Representation from Optimal Coded-Aperture for Unsupervised Classification of Hyperspectral Imagery

TL;DR

This paper introduces a low-rank subspace representation method for clustering hyperspectral images directly from compressive measurements obtained via coded aperture snapshot spectral imagers, improving clustering accuracy without reconstructing full images.

Contribution

The paper proposes a novel low-rank subspace representation algorithm tailored for compressive spectral measurements, enabling unsupervised clustering with guaranteed global optimality.

Findings

Accurate clustering demonstrated on real hyperspectral datasets.

Method outperforms existing approaches in clustering accuracy.

Efficiently handles high-dimensional compressive measurements.

Abstract

This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach.